Lab10
Matthew
2/11/2023
First
candy_file <- "candy-data.csv"
candy <- read.csv(candy_file, row.names=1)
head(candy)## chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand 1 0 1 0 0 1
## 3 Musketeers 1 0 0 0 1 0
## One dime 0 0 0 0 0 0
## One quarter 0 0 0 0 0 0
## Air Heads 0 1 0 0 0 0
## Almond Joy 1 0 0 1 0 0
## hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand 0 1 0 0.732 0.860 66.97173
## 3 Musketeers 0 1 0 0.604 0.511 67.60294
## One dime 0 0 0 0.011 0.116 32.26109
## One quarter 0 0 0 0.011 0.511 46.11650
## Air Heads 0 0 0 0.906 0.511 52.34146
## Almond Joy 0 1 0 0.465 0.767 50.34755Q1. How many different candy types are in this dataset?
dim(candy)## [1] 85 12There are 85 different candy types
Q2. How many fruity candy types are in the dataset?
dim(candy)
sum(candy$fruity,na.rm=TRUE)## [1] 38There are 38 fruity candy types in the database.
candy["Twix", ]$winpercent## [1] 81.64291Q3. What is your favorite candy in the dataset and what is it’s winpercent value?
candy["Sour Patch Kids", ]$winpercent## [1] 59.864My favorite candy in the dataset is Sour Patch Kids and it has a winpercent value of 59.864%.
Q4. What is the winpercent value for “Kit Kat”?
candy["Kit Kat",]$winpercent## [1] 76.7686Kit Kat has a win percent of 76.7686%.
Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?
candy["Tootsie Roll Snack Bars",]$winpercent## [1] 49.6535Tootsie Roll Snack Bars has a win percent of 49.6535%.
library("skimr")
skim(candy)| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?
p100 because compared to all the other p value, that value has most of the values close to 1 while the other has most values with values that are 0.0.
Q7. What do you think a zero and one represent for the candy$chocolate column?
If it has chocolate or not in the candy.
Q8. Plot a histogram of winpercent values
hist(candy$winpercent)
Q9. Is the distribution of winpercent values symmetrical?
No, it seems like the values are more skewed to the left
Q10. Is the center of the distribution above or below 50%?
The center of distribution seem to be below 50%.
Q11. On average is chocolate candy higher or lower ranked than fruit candy?
choc_per <- candy$winpercent[as.logical(candy$chocolate)]
choc_per## [1] 66.97173 67.60294 50.34755 56.91455 38.97504 55.37545 62.28448 56.49050
## [9] 59.23612 57.21925 76.76860 71.46505 66.57458 55.06407 73.09956 60.80070
## [17] 64.35334 47.82975 54.52645 70.73564 66.47068 69.48379 81.86626 84.18029
## [25] 73.43499 72.88790 65.71629 34.72200 37.88719 76.67378 59.52925 48.98265
## [33] 43.06890 45.73675 49.65350 81.64291 49.52411mean(choc_per)## [1] 60.92153fruit_per <- candy$winpercent[as.logical(candy$fruity)]
fruit_per## [1] 52.34146 34.51768 36.01763 24.52499 42.27208 39.46056 43.08892 39.18550
## [9] 46.78335 57.11974 51.41243 42.17877 28.12744 41.38956 39.14106 52.91139
## [17] 46.41172 55.35405 22.44534 39.44680 41.26551 37.34852 35.29076 42.84914
## [25] 63.08514 55.10370 45.99583 59.86400 52.82595 67.03763 34.57899 27.30386
## [33] 54.86111 48.98265 47.17323 45.46628 39.01190 44.37552mean(fruit_per)## [1] 44.11974Chocolate on average is higher ranked than fruity candy.
Q12. Is this difference statistically significant?
t.test(choc_per,fruit_per)##
## Welch Two Sample t-test
##
## data: choc_per and fruit_per
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.44563 22.15795
## sample estimates:
## mean of x mean of y
## 60.92153 44.11974It is statistacally significant since the p value is 2.9e-08, which is much lower than 0.05, which is usually the accepted value of when data is statistcally significant.
Q13. What are the five least liked candy types in this set?
head(candy[order(candy$winpercent),], n=15)## chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip 0 1 0 0 0
## Boston Baked Beans 0 0 0 1 0
## Chiclets 0 1 0 0 0
## Super Bubble 0 1 0 0 0
## Jawbusters 0 1 0 0 0
## Root Beer Barrels 0 0 0 0 0
## Sugar Daddy 0 0 1 0 0
## One dime 0 0 0 0 0
## Sugar Babies 0 0 1 0 0
## Haribo Happy Cola 0 0 0 0 0
## Caramel Apple Pops 0 1 1 0 0
## Strawberry bon bons 0 1 0 0 0
## Sixlets 1 0 0 0 0
## Ring pop 0 1 0 0 0
## Chewey Lemonhead Fruit Mix 0 1 0 0 0
## crispedricewafer hard bar pluribus sugarpercent
## Nik L Nip 0 0 0 1 0.197
## Boston Baked Beans 0 0 0 1 0.313
## Chiclets 0 0 0 1 0.046
## Super Bubble 0 0 0 0 0.162
## Jawbusters 0 1 0 1 0.093
## Root Beer Barrels 0 1 0 1 0.732
## Sugar Daddy 0 0 0 0 0.418
## One dime 0 0 0 0 0.011
## Sugar Babies 0 0 0 1 0.965
## Haribo Happy Cola 0 0 0 1 0.465
## Caramel Apple Pops 0 0 0 0 0.604
## Strawberry bon bons 0 1 0 1 0.569
## Sixlets 0 0 0 1 0.220
## Ring pop 0 1 0 0 0.732
## Chewey Lemonhead Fruit Mix 0 0 0 1 0.732
## pricepercent winpercent
## Nik L Nip 0.976 22.44534
## Boston Baked Beans 0.511 23.41782
## Chiclets 0.325 24.52499
## Super Bubble 0.116 27.30386
## Jawbusters 0.511 28.12744
## Root Beer Barrels 0.069 29.70369
## Sugar Daddy 0.325 32.23100
## One dime 0.116 32.26109
## Sugar Babies 0.767 33.43755
## Haribo Happy Cola 0.465 34.15896
## Caramel Apple Pops 0.325 34.51768
## Strawberry bon bons 0.058 34.57899
## Sixlets 0.081 34.72200
## Ring pop 0.965 35.29076
## Chewey Lemonhead Fruit Mix 0.511 36.01763The five least liked cany types in the set are Nik L Nip, Boston Baked Beans, Chiclets, Super Bubble, and Jawbusters
Q14. What are the top 5 all time favorite candy types out of this set?
tail(candy[order(candy$winpercent),], n=15)## chocolate fruity caramel peanutyalmondy nougat
## M&M's 1 0 0 0 0
## 100 Grand 1 0 1 0 0
## Starburst 0 1 0 0 0
## 3 Musketeers 1 0 0 0 1
## Peanut M&Ms 1 0 0 1 0
## Nestle Butterfinger 1 0 0 1 0
## Peanut butter M&M's 1 0 0 1 0
## Reese's stuffed with pieces 1 0 0 1 0
## Milky Way 1 0 1 0 1
## Reese's pieces 1 0 0 1 0
## Snickers 1 0 1 1 1
## Kit Kat 1 0 0 0 0
## Twix 1 0 1 0 0
## Reese's Miniatures 1 0 0 1 0
## Reese's Peanut Butter cup 1 0 0 1 0
## crispedricewafer hard bar pluribus sugarpercent
## M&M's 0 0 0 1 0.825
## 100 Grand 1 0 1 0 0.732
## Starburst 0 0 0 1 0.151
## 3 Musketeers 0 0 1 0 0.604
## Peanut M&Ms 0 0 0 1 0.593
## Nestle Butterfinger 0 0 1 0 0.604
## Peanut butter M&M's 0 0 0 1 0.825
## Reese's stuffed with pieces 0 0 0 0 0.988
## Milky Way 0 0 1 0 0.604
## Reese's pieces 0 0 0 1 0.406
## Snickers 0 0 1 0 0.546
## Kit Kat 1 0 1 0 0.313
## Twix 1 0 1 0 0.546
## Reese's Miniatures 0 0 0 0 0.034
## Reese's Peanut Butter cup 0 0 0 0 0.720
## pricepercent winpercent
## M&M's 0.651 66.57458
## 100 Grand 0.860 66.97173
## Starburst 0.220 67.03763
## 3 Musketeers 0.511 67.60294
## Peanut M&Ms 0.651 69.48379
## Nestle Butterfinger 0.767 70.73564
## Peanut butter M&M's 0.651 71.46505
## Reese's stuffed with pieces 0.651 72.88790
## Milky Way 0.651 73.09956
## Reese's pieces 0.651 73.43499
## Snickers 0.651 76.67378
## Kit Kat 0.511 76.76860
## Twix 0.906 81.64291
## Reese's Miniatures 0.279 81.86626
## Reese's Peanut Butter cup 0.651 84.18029The top 5 all time favorite candy types are Snickers, Kit Kat, Twix, Reese’s Minatures, and Reese’s Peanut Butter cup
Q15. Make a first barplot of candy ranking based on winpercent values.
library(ggplot2)ggplot(candy) +
aes(winpercent, rownames(candy),winpercent) +
geom_col() 
Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col() 
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols) 
Q17. What is the worst ranked chocolate candy?
Sixlets
Q18. What is the best ranked fruity candy?
Starburst
library("ggrepel")# How about a plot of price vs win
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3, max.overlaps = 5)## Warning: ggrepel: 50 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?
Reeses Minature since it has over 80% winpercent with a little over 25% pricepercent.
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )## pricepercent winpercent
## Nik L Nip 0.976 22.44534
## Nestle Smarties 0.976 37.88719
## Ring pop 0.965 35.29076
## Hershey's Krackel 0.918 62.28448
## Hershey's Milk Chocolate 0.918 56.49050The top 5 most expensive candies are Nik L Lip, Ring pop, Nestl Smarties, Hershey Krackel, and Hersheys Milk Chocolate. The most unpopular candy of these are Nik L Nip
Q21. Make a barplot again with geom_col() this time using pricepercent and then improve this step by step, first ordering the x-axis by value and finally making a so called “dot chat” or “lollipop” chart by swapping geom_col() for geom_point() + geom_segment().
# Make a lollipop chart of pricepercent
ggplot(candy) +
aes(pricepercent, reorder(rownames(candy), pricepercent)) +
geom_segment(aes(yend = reorder(rownames(candy), pricepercent),
xend = 0), col="gray40") +
geom_point()
library(corrplot)## corrplot 0.92 loadedcij <- cor(candy)
corrplot(cij)
Q23. Similarly, what two variables are most positively correlated?
Chocolate and bar or chocolate and winpercent.
pca <- prcomp(candy, scale=TRUE)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000plot(pca$x[,1:2],col=my_cols,pch=16)
# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols)
p
p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")## Warning: ggrepel: 39 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(p)par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")
Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you?
The original variables that are picked up strongly by PC1 in positive direction are fruity, hard, and pluribus.